Inference-time Policy Steering via Vision and Touch
ViTaL framework uses bi-level optimization combining vision and touch to steer generative robot policies at inference time, significantly improving success in contact-rich manipulation tasks.
[2606.14981] Inference-time Policy Steering via Vision and Touch
[Submitted on 12 Jun 2026]
Title:Inference-time Policy Steering via Vision and Touch
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Abstract:Inference-time steering adapts pre-trained generative robot policies during deployment by verifying candidate actions before execution. While prior methods typically perform this verification only with visual observations, vision alone is often insufficient for contact-rich manipulation, where success depends on both global task progress and subtle local interactions such as contact force. We introduce ViTaL, a visuo-tactile inference-time steering framework that formulates multimodal guidance as a bi-level optimization problem. At the high level, visual sampling-and-verification performs long-horizon mode selection, deciding what behavior the robot should execute. At the low level, tactile-guided diffusion editing refines the selected action sequence over a shorter horizon to satisfy local contact requirements. To support outcome-based steering, ViTaL learns a visuo-tactile latent world model and employs semantically aligned visual and tactile verifiers, including a novel text-conditioned tactile reward that scores predicted tactile futures directly in latent space. Across three real-world contact-rich manipulation tasks, ViTaL improves overall success by 51% over the base policy, outperforms unimodal steering by at least 33%, and exceeds naive multimodal fusion by at least 20%. Website: this https URL.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.14981 [cs.RO]
(or arXiv:2606.14981v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14981
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yilin Wu [view email] [v1] Fri, 12 Jun 2026 22:03:21 UTC (17,002 KB)
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